Pseudo-value Based Mean Cumulative Count Regression

The mean cumulative function (MCF) summarizes how events accumulate over time for a recurrent or multi-component endpoint.

The MCF, and its integral over a given time horizon, the area under the MCF (AUMCF), provide interpretable summaries of recurrent-event burden in the presence of right-censoring and terminal events.

Existing approaches for these estimands have focused primarily on nonparametric treatment comparisons, covariate-adjusted augmentation, and linearized test statistics.

Herein, we propose a pseudo-value-based regression approach for estimating covariate effects on the MCF and AUMCF at a fixed truncation time.

The proposed method uses influence-function-based pseudo-values as regression outcomes, allowing estimation with standard generalized estimating equation machinery and, under an identity link, ordinary least squares.

Through simulation studies, we evaluate estimation accuracy, confidence interval coverage, type I error control, and power across a range of recurrent-event settings.

We demonstrate the utility of the proposed covariate adjustment procedure through an application to the ORATORIO clinical trial, evaluating the safety and efficacy of ocrelizumab for the treatment of primary progressive multiple sclerosis.

Overall, pseudo-value-based regression provides a simple and interpretable framework for modeling covariate effects on cumulative recurrent-event burden over time.